




import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType





class 					snake_case_   (     snake_case_	):



				'''simple docstring'''





				def __init__(      self							:   Optional[Any]      ,							__magic_name__							:   Optional[int]							)							->			List[str]:
											lowerCamelCase_			:    List[str]   						=      data





				def __iter__(      self							:   Optional[int]							)							->			str:
											for element in self.data:
																		yield element



def        __a							(  __UpperCAmelCase		:						str=True      ) ->					str:
							"""simple docstring"""




							lowerCamelCase_			:    Optional[int]   						=      Accelerator(even_batches=__UpperCAmelCase      )
							assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
							return accelerator



def        __a							(  __UpperCAmelCase		:						Accelerator   ,		__UpperCAmelCase		:						int   ,		__UpperCAmelCase		:						int   ,		__UpperCAmelCase		:						bool = False      ) ->					int:
							"""simple docstring"""




							if iterable:
														lowerCamelCase_			:    List[str]   						=      DummyIterableDataset(torch.as_tensor(range(__UpperCAmelCase      )      )      )
							else:
														lowerCamelCase_			:    List[Any]   						=      TensorDataset(torch.as_tensor(range(__UpperCAmelCase      )      )      )

							lowerCamelCase_			:    Optional[int]   						=      DataLoader(__UpperCAmelCase   ,		batch_size=__UpperCAmelCase      )
							lowerCamelCase_			:    str   						=      accelerator.prepare(__UpperCAmelCase      )

							return dl



def        __a							(  __UpperCAmelCase		:						Accelerator   ,		__UpperCAmelCase		:						int   ,		__UpperCAmelCase		:						int   ,		__UpperCAmelCase		:						List[int]   ,		__UpperCAmelCase		:						List[int]   ,		) ->					Any:
							"""simple docstring"""




							lowerCamelCase_			:    List[Any]   						=      create_dataloader(accelerator=__UpperCAmelCase   ,		dataset_size=__UpperCAmelCase   ,		batch_size=__UpperCAmelCase      )

							lowerCamelCase_			:    Union[str, Any]   						=      [len(batch[0]      ) for batch in dl]

							if accelerator.process_index == 0:
														assert batch_sizes == process_0_expected_batch_sizes
							elif accelerator.process_index == 1:
														assert batch_sizes == process_1_expected_batch_sizes



def        __a							(  ) ->					List[str]:
							"""simple docstring"""




							lowerCamelCase_			:    Union[str, Any]   						=      create_accelerator()

							# without padding, we would expect a different number of batches
							verify_dataloader_batch_sizes(
							    __UpperCAmelCase   ,		dataset_size=3   ,		batch_size=1   ,		process_0_expected_batch_sizes=[1, 1]   ,		process_1_expected_batch_sizes=[1, 1]   ,		)

							# without padding, we would expect the same number of batches, but different sizes
							verify_dataloader_batch_sizes(
							    __UpperCAmelCase   ,		dataset_size=7   ,		batch_size=2   ,		process_0_expected_batch_sizes=[2, 2]   ,		process_1_expected_batch_sizes=[2, 2]   ,		)



def        __a							(  ) ->					Any:
							"""simple docstring"""




							lowerCamelCase_			:    Dict   						=      create_accelerator(even_batches=__UpperCAmelCase      )

							verify_dataloader_batch_sizes(
							    __UpperCAmelCase   ,		dataset_size=3   ,		batch_size=1   ,		process_0_expected_batch_sizes=[1, 1]   ,		process_1_expected_batch_sizes=[1]   ,		)

							verify_dataloader_batch_sizes(
							    __UpperCAmelCase   ,		dataset_size=7   ,		batch_size=2   ,		process_0_expected_batch_sizes=[2, 2]   ,		process_1_expected_batch_sizes=[2, 1]   ,		)



def        __a							(  ) ->					Any:
							"""simple docstring"""




							lowerCamelCase_			:    int   						=      create_accelerator(even_batches=__UpperCAmelCase      )

							lowerCamelCase_			:    List[Any]   						=      torch.nn.Linear(1   ,		1      )
							lowerCamelCase_			:    str   						=      accelerator.prepare(__UpperCAmelCase      )

							lowerCamelCase_			:    Optional[Any]   						=      create_dataloader(__UpperCAmelCase   ,		dataset_size=3   ,		batch_size=1      )

							lowerCamelCase_			:    Optional[Any]   						=      []
							with accelerator.join_uneven_inputs([ddp_model]      ):
														for batch_idx, batch in enumerate(__UpperCAmelCase      ):
																					lowerCamelCase_			:    List[Any]   						=      ddp_model(batch[0].float()      )
																					lowerCamelCase_			:    int   						=      output.sum()
																					loss.backward()
																					batch_idxs.append(__UpperCAmelCase      )

							accelerator.wait_for_everyone()

							if accelerator.process_index == 0:
														assert batch_idxs == [0, 1]
							elif accelerator.process_index == 1:
														assert batch_idxs == [0]



def        __a							(  __UpperCAmelCase		:						Optional[int]      ) ->					str:
							"""simple docstring"""




							with warnings.catch_warnings(record=__UpperCAmelCase      ) as w:
														with accelerator.join_uneven_inputs([Mock()]      ):
																					pass

														assert issubclass(w[-1].category   ,		__UpperCAmelCase      )
														assert "only supported for multi-GPU" in str(w[-1].message      )



def        __a							(  ) ->					Optional[Any]:
							"""simple docstring"""




							lowerCamelCase_			:    Optional[int]   						=      True
							lowerCamelCase_			:    Optional[int]   						=      False
							lowerCamelCase_			:    Any   						=      create_accelerator(even_batches=__UpperCAmelCase      )
							lowerCamelCase_			:    Optional[int]   						=      torch.nn.Linear(1   ,		1      )
							lowerCamelCase_			:    Optional[Any]   						=      accelerator.prepare(__UpperCAmelCase      )
							lowerCamelCase_			:    Optional[int]   						=      create_dataloader(__UpperCAmelCase   ,		dataset_size=3   ,		batch_size=1      )
							lowerCamelCase_			:    Dict   						=      create_dataloader(__UpperCAmelCase   ,		dataset_size=3   ,		batch_size=1      )

							with accelerator.join_uneven_inputs([ddp_model]   ,		even_batches=__UpperCAmelCase      ):
														lowerCamelCase_			:    Union[str, Any]   						=      train_dl.batch_sampler.even_batches
														lowerCamelCase_			:    Union[str, Any]   						=      valid_dl.batch_sampler.even_batches

							assert train_dl_overridden_value == overridden_even_batches
							assert valid_dl_overridden_value == overridden_even_batches
							assert train_dl.batch_sampler.even_batches == default_even_batches
							assert valid_dl.batch_sampler.even_batches == default_even_batches



def        __a							(  ) ->					int:
							"""simple docstring"""




							lowerCamelCase_			:    Optional[Any]   						=      True
							lowerCamelCase_			:    Optional[int]   						=      False
							lowerCamelCase_			:    str   						=      create_accelerator(even_batches=__UpperCAmelCase      )
							lowerCamelCase_			:    int   						=      torch.nn.Linear(1   ,		1      )
							lowerCamelCase_			:    str   						=      accelerator.prepare(__UpperCAmelCase      )
							create_dataloader(__UpperCAmelCase   ,		dataset_size=3   ,		batch_size=1   ,		iterable=__UpperCAmelCase      )
							lowerCamelCase_			:    Any   						=      create_dataloader(__UpperCAmelCase   ,		dataset_size=3   ,		batch_size=1      )

							with warnings.catch_warnings():
														warnings.filterwarnings("ignore"      )
														try:
																					with accelerator.join_uneven_inputs([ddp_model]   ,		even_batches=__UpperCAmelCase      ):
																												lowerCamelCase_			:    str   						=      batch_dl.batch_sampler.even_batches
														except AttributeError:
																					# ensure attribute error is not raised when processing iterable dl
																					raise AssertionError

							assert batch_dl_overridden_value == overridden_even_batches
							assert batch_dl.batch_sampler.even_batches == default_even_batches



def        __a							(  ) ->					Optional[int]:
							"""simple docstring"""




							lowerCamelCase_			:    List[str]   						=      create_accelerator()
							lowerCamelCase_			:    Union[str, Any]   						=      torch.nn.Linear(1   ,		1      )
							lowerCamelCase_			:    Any   						=      accelerator.prepare(__UpperCAmelCase      )
							create_dataloader(__UpperCAmelCase   ,		dataset_size=3   ,		batch_size=1   ,		iterable=__UpperCAmelCase      )

							with warnings.catch_warnings(record=__UpperCAmelCase      ) as w:
														with accelerator.join_uneven_inputs([ddp_model]   ,		even_batches=__UpperCAmelCase      ):
																					pass

														assert issubclass(w[-1].category   ,		__UpperCAmelCase      )
														assert "only supported for map-style datasets" in str(w[-1].message      )



def        __a							(  ) ->					List[Any]:
							"""simple docstring"""




							lowerCamelCase_			:    int   						=      create_accelerator()

							accelerator.print("Test that even_batches variable ensures uniform batches across processes"      )
							test_default_ensures_even_batch_sizes()

							accelerator.print("Run tests with even_batches disabled"      )
							test_can_disable_even_batches()

							accelerator.print("Test joining uneven inputs"      )
							test_can_join_uneven_inputs()

							accelerator.print("Test overriding even_batches when joining uneven inputs"      )
							test_join_can_override_even_batches()

							accelerator.print("Test overriding even_batches for mixed dataloader types"      )
							test_join_can_override_for_mixed_type_dataloaders()

							accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders"      )
							test_join_raises_warning_for_iterable_when_overriding_even_batches()

							accelerator.print("Test join with non DDP distributed raises warning"      )
							lowerCamelCase_			:    int   						=      accelerator.state.distributed_type
							lowerCamelCase_			:    Optional[int]   						=      DistributedType.FSDP
							test_join_raises_warning_for_non_ddp_distributed(__UpperCAmelCase      )
							lowerCamelCase_			:    Union[str, Any]   						=      original_state


if __name__ == "__main__":
	main()






'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						lowerCamelCase_				(       snake_case_     ,					unittest.TestCase      ):
						_lowerCAmelCase  : str						  =					KandinskyInpaintPipeline
						_lowerCAmelCase  : List[Any]						  =					['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
						_lowerCAmelCase  : int						  =					[
						    'prompt',
						    'negative_prompt',
						    'image_embeds',
						    'negative_image_embeds',
						    'image',
						    'mask_image',
						]
						_lowerCAmelCase  : List[str]						  =					[
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						_lowerCAmelCase  : Union[str, Any]						  =					False







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													return self.time_input_dim







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return self.time_input_dim * 4







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return 1_00







						@property
						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''					)
													return tokenizer







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      MCLIPConfig(
													    numDims=self.cross_attention_dim					,     transformerDimensions=self.text_embedder_hidden_size					,     hidden_size=self.text_embedder_hidden_size					,     intermediate_size=37					,     num_attention_heads=4					,     num_hidden_layers=5					,     vocab_size=10_05					,     )

													SCREAMING_SNAKE_CASE       :   Any         =      MultilingualCLIP(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      text_encoder.eval()

													return text_encoder







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)

													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''in_channels''': 9,
													    # Out channels is double in channels because predicts mean and variance
													    '''out_channels''': 8,
													    '''addition_embed_type''': '''text_image''',
													    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
													    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
													    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
													    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
													    '''layers_per_block''': 1,
													    '''encoder_hid_dim''': self.text_embedder_hidden_size,
													    '''encoder_hid_dim_type''': '''text_image_proj''',
													    '''cross_attention_dim''': self.cross_attention_dim,
													    '''attention_head_dim''': 4,
													    '''resnet_time_scale_shift''': '''scale_shift''',
													    '''class_embed_type''': None,
													}

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      UNetaDConditionModel(**lowerCAmelCase__					)
													return model







						@property
						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													return {
													    "block_out_channels": [32, 64],
													    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
													    "in_channels": 3,
													    "latent_channels": 4,
													    "layers_per_block": 1,
													    "norm_num_groups": 8,
													    "norm_type": "spatial",
													    "num_vq_embeddings": 12,
													    "out_channels": 3,
													    "up_block_types": [
													        "AttnUpDecoderBlock2D",
													        "UpDecoderBlock2D",
													    ],
													    "vq_embed_dim": 4,
													}







						@property
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      VQModel(**self.dummy_movq_kwargs					)
													return model







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.dummy_text_encoder
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.dummy_tokenizer
													SCREAMING_SNAKE_CASE       :   int         =      self.dummy_unet
													SCREAMING_SNAKE_CASE       :   str         =      self.dummy_movq

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      DDIMScheduler(
													    num_train_timesteps=10_00					,     beta_schedule='''linear'''					,     beta_start=0.0_0085					,     beta_end=0.012					,     clip_sample=lowerCAmelCase__					,     set_alpha_to_one=lowerCAmelCase__					,     steps_offset=1					,     prediction_type='''epsilon'''					,     thresholding=lowerCAmelCase__					,     )

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      {
													    '''text_encoder''': text_encoder,
													    '''tokenizer''': tokenizer,
													    '''unet''': unet,
													    '''scheduler''': scheduler,
													    '''movq''': movq,
													}

													return components







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   List[Any]=0					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(seed + 1					)					).to(lowerCAmelCase__					)
													# create init_image
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      floats_tensor((1, 3, 64, 64)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      image.cpu().permute(0					,     2					,     3					,     1					)[0]
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      Image.fromarray(np.uinta(lowerCAmelCase__					)					).convert('''RGB'''					).resize((2_56, 2_56)					)
													# create mask
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((64, 64)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   str         =      0

													if str(lowerCAmelCase__					).startswith('''mps'''					):
																				SCREAMING_SNAKE_CASE       :   Tuple         =      torch.manual_seed(lowerCAmelCase__					)
													else:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      torch.Generator(device=lowerCAmelCase__					).manual_seed(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''prompt''': '''horse''',
													    '''image''': init_image,
													    '''mask_image''': mask,
													    '''image_embeds''': image_embeds,
													    '''negative_image_embeds''': negative_image_embeds,
													    '''generator''': generator,
													    '''height''': 64,
													    '''width''': 64,
													    '''num_inference_steps''': 2,
													    '''guidance_scale''': 4.0,
													    '''output_type''': '''np''',
													}
													return inputs







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      '''cpu'''

													SCREAMING_SNAKE_CASE       :   Dict         =      self.get_dummy_components()

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.pipeline_class(**lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   int         =      pipe.to(lowerCAmelCase__					)

													pipe.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   str         =      pipe(**self.get_dummy_inputs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   int         =      output.images

													SCREAMING_SNAKE_CASE       :   int         =      pipe(
													    **self.get_dummy_inputs(lowerCAmelCase__					)					,     return_dict=lowerCAmelCase__					,     )[0]

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE       :   Dict         =      image_from_tuple[0, -3:, -3:, -1]

													print(F"""image.shape {image.shape}"""					)

													assert image.shape == (1, 64, 64, 3)

													SCREAMING_SNAKE_CASE       :   List[str]         =      np.array(
													    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]					)

													assert (
													    np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
													assert (
													    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													super().test_inference_batch_single_identical(expected_max_diff=3e-3					)


@slow
@require_torch_gpu
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''					)

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((7_68, 7_68)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      0

													SCREAMING_SNAKE_CASE       :   Dict         =      '''a hat'''

													SCREAMING_SNAKE_CASE       :   str         =      KandinskyPriorPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-prior'''					,     torch_dtype=torch.floataa					)
													pipe_prior.to(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      KandinskyInpaintPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-inpaint'''					,     torch_dtype=torch.floataa					)
													SCREAMING_SNAKE_CASE       :   Dict         =      pipeline.to(lowerCAmelCase__					)
													pipeline.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.Generator(device='''cpu'''					).manual_seed(0					)
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Any         =      pipe_prior(
													    lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=5					,     negative_prompt=''''''					,     ).to_tuple()

													SCREAMING_SNAKE_CASE       :   Tuple         =      pipeline(
													    lowerCAmelCase__					,     image=lowerCAmelCase__					,     mask_image=lowerCAmelCase__					,     image_embeds=lowerCAmelCase__					,     negative_image_embeds=lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=1_00					,     height=7_68					,     width=7_68					,     output_type='''np'''					,     )

													SCREAMING_SNAKE_CASE       :   Tuple         =      output.images[0]

													assert image.shape == (7_68, 7_68, 3)

													assert_mean_pixel_difference(lowerCAmelCase__					,     lowerCAmelCase__					)




